The Availability and Consistency of Dengue Surveillance Data Provided Online by the World Health Organization

Background: The use of high quality disease surveillance data has become increasingly important for public health action against new threats. In response, countries have developed a wide range of disease surveillance systems enabled by technological advancements. The heterogeneity and complexity of country data systems have caused a growing need for international organizations such as the World Health Organization (WHO) to coordinate the standardization, integration, and dissemination of country disease data at the global level for research and policy. The availability and consistency of currently available disease surveillance data at the global level are unclear. We investigated this for dengue surveillance data provided online by the WHO. Methods and Findings: We extracted all dengue surveillance data provided online by WHO Headquarters and Regional Offices (RO’s). We assessed the availability and consistency of these data by comparing indicators within and between sources. We also assessed the consistency of dengue data provided online by two example countries (Brazil and Indonesia). Data were available from WHO for 100 countries since 1955 representing a total of 23 million dengue cases and 82 thousand deaths ever reported to WHO. The availability of data on DengueNet and some RO’s declined dramatically after 2005. Consistency was lacking between sources (84% across all indicators representing a discrepancy of almost half a million cases). Within sources, data at high spatial resolution were often incomplete. Conclusions: The decline of publicly available, integrated dengue surveillance data at the global level will limit opportunities for research, policy, and advocacy. A new financial and operational framework will be necessary for innovation and for the continued availability of integrated country disease data at the global level

The impact of front-of-pack marketing attributes versus nutrition and health information on parents’ food choices

© 2017 Front-of-pack attributes have the potential to affect parents’ food choices on behalf of their children and form one avenue through which strategies to address the obesogenic environment can be developed. Previous work has focused on the isolated effects of nutrition and health information (e.g. labeling systems, health claims), and how parents trade off this information against co-occurring marketing features (e.g. product imagery, cartoons) is unclear. A Discrete Choice Experiment was utilized to understand how front-of-pack nutrition, health and marketing attributes, as well as pricing, influenced parents’ choices of cereal for their child. Packages varied with respect to the two elements of the Australian Health Star Rating system (stars and nutrient facts panel), along with written claims, product visuals, additional visuals, and price. A total of 520 parents (53% male) with a child aged between five and eleven years were recruited via an online panel company and completed the survey. Product visuals, followed by star ratings, were found to be the most significant attributes in driving choice, while written claims and other visuals were the least significant. Use of the Health Star Rating (HSR) system and other features were related to the child's fussiness level and parents’ concerns about their child's weight with parents of fussy children, in particular, being less influenced by the HSR star information and price. The findings suggest that front-of-pack health labeling systems can affect choice when parents trade this information off against marketing attributes, yet some marketing attributes can be more influential, and not all parents utilize this information in the same way

Computational characterization of transient strain-transcending immunity against influenza A

The enigmatic observation that the rapidly evolving influenza A (H3N2) virus exhibits, at any given time, a limited standing genetic diversity has been an impetus for much research. One of the first generative computational models to successfully recapitulate this pattern of consistently constrained diversity posits the existence of a strong and short-lived straintranscending immunity. Building on that model, we explored a much broader set of scenarios (parameterizations) of a transient strain-transcending immunity, ran long-term simulations of each such scenario, and assessed its plausibility with respect to a set of known or estimated influenza empirical measures. We evaluated simulated outcomes using a variety of measures, both epidemiological (annual attack rate, epidemic duration, reproductive number, and peak weekly incidence), and evolutionary (pairwise antigenic diversity, fixation rate, most recent common ancestor, and kappa, which quantifies the potential for antigenic evolution). Taking cumulative support from all these measures, we show which parameterizations of strain-transcending immunity are plausible with respect to the set of empirically derived target values. We conclude that strain-transcending immunity which is milder and longer lasting than previously suggested is more congruent with the observed short- and long-term behavior of influenza

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

A step counting hill climbing algorithm

This paper presents a new single-parameter local search heuristic named Step Counting Hill Climbing algorithm (SCHC). It is a very simple method in which the current cost serves as an acceptance bound for a number of consecutive steps. This is the only parameter in the method that should be set up by the user. Furthermore, the counting of steps can be organized in different ways; therefore the proposed method can generate a large number of variants and also extensions. In this paper, we investigate the behaviour of the three basic variants of SCHC on the university exam timetabling problem. Our experiments demonstrate that the proposed method shares the main properties with the Late Acceptance Hill Climbing method, namely its convergence time is proportional to the value of its parameter and a non-linear rescaling of a problem does not affect its search performance. However, our new method has two additional advantages: a more flexible acceptance condition and better overall performance. In this study we compare the new method with Late Acceptance Hill Climbing, Simulated Annealing and Great Deluge Algorithm. The Step Counting Hill Climbing has shown the strongest performance on the most of our benchmark problems used

Sparse, continuous policy representations for uniform online bin packing via regression of interpolants

Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation of policies. One of the main limitations of some previously-used policy representations is the trade-off between locality and granularity in the associated search space. In this article, we adopt an interpolation-based representation which has the jointly-desirable properties of being sparse and continuous (i.e. exhibits good genotype-to-phenotype locality). In contrast to previous approaches, the policy space is searchable via real-valued optimization methods. Packing policies using five different interpolation methods are comprehensively compared against a range of existing methods from the literature, and it is determined that the proposed method scales to larger instances than those in the literature

Coherent spinor dynamics in a spin-1 Bose condensate

Collisions in a thermal gas are perceived as random or incoherent as a consequence of the large numbers of initial and final quantum states accessible to the system. In a quantum gas, e.g. a Bose-Einstein condensate or a degenerate Fermi gas, the phase space accessible to low energy collisions is so restricted that collisions be-come coherent and reversible. Here, we report the observation of coherent spin-changing collisions in a gas of spin-1 bosons. Starting with condensates occupying two spin states, a condensate in the third spin state is coherently and reversibly created by atomic collisions. The observed dynamics are analogous to Josephson oscillations in weakly connected superconductors and represent a type of matter-wave four-wave mixing. The spin-dependent scattering length is determined from these oscillations to be -1.45(18) Bohr. Finally, we demonstrate coherent control of the evolution of the system by applying differential phase shifts to the spin states using magnetic fields.Comment: 19 pages, 3 figure

The impact of the demographic transition on dengue in Thailand: Insights from a statistical analysis and mathematical modeling

Background: An increase in the average age of dengue hemorrhagic fever (DHF) cases has been reported in Thailand. The cause of this increase is not known. Possible explanations include a reduction in transmission due to declining mosquito populations, declining contact between human and mosquito, and changes in reporting. We propose that a demographic shift toward lower birth and death rates has reduced dengue transmission and lengthened the interval between large epidemics. Methods and Findings: Using data from each of the 72 provinces of Thailand, we looked for associations between force of infection (a measure of hazard, defined as the rate per capita at which susceptible individuals become infected) and demographic and climactic variables. We estimated the force of infection from the age distribution of cases from 1985 to 2005. We find that the force of infection has declined by 2% each year since a peak in the late 1970s and early 1980s. Contrary to recent findings suggesting that the incidence of DHF has increased in Thailand, we find a small but statistically significant decline in DHF incidence since 1985 in a majority of provinces. The strongest predictor of the change in force of infection and the mean force of infection is the median age of the population. Using mathematical simulations of dengue transmission we show that a reduced birth rate and a shift in the population's age structure can explain the shift in the age distribution of cases, reduction of the force of infection, and increase in the periodicity of multiannual oscillations of DHF incidence in the absence of other changes. Conclusions: Lower birth and death rates decrease the flow of susceptible individuals into the population and increase the longevity of immune individuals. The increase in the proportion of the population that is immune increases the likelihood that an infectious mosquito will feed on an immune individual, reducing the force of infection. Though the force of infection has decreased by half, we find that the critical vaccination fraction has not changed significantly, declining from an average of 85% to 80%. Clinical guidelines should consider the impact of continued increases in the age of dengue cases in Thailand. Countries in the region lagging behind Thailand in the demographic transition may experience the same increase as their population ages. The impact of demographic changes on the force of infection has been hypothesized for other diseases, but, to our knowledge, this is the first observation of this phenomenon